Context-based precoding matrix computations for radio access network for 5g or other next generation network

ABSTRACT

Precoding matrix computations for a large number of antenna arrays can be used to generate efficiencies within a wireless network. Utilizing network topology and context data in conjunction with known available network resources and mobile device measurements can facilitate gains in power and spectral efficiency and reduction in computation complexity posed by current procedures. To take advantage of multiple paths, the precoding matrix can be known at the radio units for each mobile device.

TECHNICAL FIELD

This disclosure relates generally to facilitating context-based pre-coding matrix computations. For example, this disclosure relates to facilitating context-based pre-coding matrix computations for a radio access network for a 5G, or other next generation network, air interface.

BACKGROUND

5th generation (5G) wireless systems represent a next major phase of mobile telecommunications standards beyond the current telecommunications standards of 4^(th) generation (4G). Rather than faster peak Internet connection speeds, 5G planning aims at higher capacity than current 4G, allowing a higher number of mobile broadband users per area unit, and allowing consumption of higher or unlimited data quantities. This would enable a large portion of the population to stream high-definition media many hours per day with their mobile devices, when out of reach of wireless fidelity hotspots. 5G research and development also aims at improved support of machine-to-machine communication, also known as the Internet of things, aiming at lower cost, lower battery consumption, and lower latency than 4G equipment.

The above-described background relating to facilitating context-based pre-coding matrix computations for a radio access network is merely intended to provide a contextual overview of some current issues, and is not intended to be exhaustive. Other contextual information may become further apparent upon review of the following detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting and non-exhaustive embodiments of the subject disclosure are described with reference to the following figures, wherein like reference numerals refer to like parts throughout the various views unless otherwise specified.

FIG. 1 illustrates an example wireless communication system in which a network node device (e.g., network node) and user equipment (UE) can implement various aspects and embodiments of the subject disclosure.

FIG. 2 illustrates an example schematic system block diagram of a radio access network intelligent controller according to one or more embodiments.

FIG. 3 illustrates an example schematic system block diagram of a context-based precoding matrix system according to one or more embodiments.

FIG. 4 illustrates an example schematic system block diagram of a context-based precoding matrix system for UEs with a larger coherence time according to one or more embodiments.

FIG. 5 illustrates an example schematic system block diagram of a context-based precoding matrix system comprising a reduced number of channels for UEs according to one or more embodiments.

FIG. 6 illustrates an example flow diagram for a method for a context-based precoding matrix for a 5G network according to one or more embodiments.

FIG. 7 illustrates an example flow diagram for a wireless network device for a context-based precoding matrix for a 5G network according to one or more embodiments.

FIG. 8 an example flow diagram for a machine-readable medium for a context-based precoding matrix for a 5G network according to one or more embodiments.

FIG. 9 illustrates an example block diagram of an example mobile handset operable to engage in a system architecture that facilitates secure wireless communication according to one or more embodiments described herein.

FIG. 10 illustrates an example block diagram of an example computer operable to engage in a system architecture that facilitates secure wireless communication according to one or more embodiments described herein.

DETAILED DESCRIPTION

In the following description, numerous specific details are set forth to provide a thorough understanding of various embodiments. One skilled in the relevant art will recognize, however, that the techniques described herein can be practiced without one or more of the specific details, or with other methods, components, materials, etc. In other instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring certain aspects.

Reference throughout this specification to “one embodiment,” or “an embodiment,” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, the appearances of the phrase “in one embodiment,” “in one aspect,” or “in an embodiment,” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

As utilized herein, terms “component,” “system,” “interface,” and the like are intended to refer to a computer-related entity, hardware, software (e.g., in execution), and/or firmware. For example, a component can be a processor, a process running on a processor, an object, an executable, a program, a storage device, and/or a computer. By way of illustration, an application running on a server and the server can be a component. One or more components can reside within a process, and a component can be localized on one computer and/or distributed between two or more computers.

Further, these components can execute from various machine-readable media having various data structures stored thereon. The components can communicate via local and/or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and/or across a network, e.g., the Internet, a local area network, a wide area network, etc. with other systems via the signal).

As another example, a component can be an apparatus with specific functionality provided by mechanical parts operated by electric or electronic circuitry; the electric or electronic circuitry can be operated by a software application or a firmware application executed by one or more processors; the one or more processors can be internal or external to the apparatus and can execute at least a part of the software or firmware application. As yet another example, a component can be an apparatus that provides specific functionality through electronic components without mechanical parts; the electronic components can include one or more processors therein to execute software and/or firmware that confer(s), at least in part, the functionality of the electronic components. In an aspect, a component can emulate an electronic component via a virtual machine, e.g., within a cloud computing system.

The words “exemplary” and/or “demonstrative” are used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and/or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive—in a manner similar to the term “comprising” as an open transition word—without precluding any additional or other elements.

As used herein, the term “infer” or “inference” refers generally to the process of reasoning about, or inferring states of, the system, environment, user, and/or intent from a set of observations as captured via events and/or data. Captured data and events can include user data, device data, environment data, data from sensors, sensor data, application data, implicit data, explicit data, etc. Inference can be employed to identify a specific context or action, or can generate a probability distribution over states of interest based on a consideration of data and events, for example.

Inference can also refer to techniques employed for composing higher-level events from a set of events and/or data. Such inference results in the construction of new events or actions from a set of observed events and/or stored event data, whether the events are correlated in close temporal proximity, and whether the events and data come from one or several event and data sources. Various classification schemes and/or systems (e.g., support vector machines, neural networks, expert systems, Bayesian belief networks, fuzzy logic, and data fusion engines) can be employed in connection with performing automatic and/or inferred action in connection with the disclosed subject matter.

In addition, the disclosed subject matter can be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques to produce software, firmware, hardware, or any combination thereof to control a computer to implement the disclosed subject matter. The term “article of manufacture” as used herein is intended to encompass a computer program accessible from any computer-readable device, machine-readable device, computer-readable carrier, computer-readable media, or machine-readable media. For example, computer-readable media can include, but are not limited to, a magnetic storage device, e.g., hard disk; floppy disk; magnetic strip(s); an optical disk (e.g., compact disk (CD), a digital video disc (DVD), a Blu-ray Disc™ (BD)); a smart card; a flash memory device (e.g., card, stick, key drive); and/or a virtual device that emulates a storage device and/or any of the above computer-readable media.

As an overview, various embodiments are described herein to facilitate context-based pre-coding matrix computations for a radio access network for a 5G air interface or other next generation networks. For simplicity of explanation, the methods (or algorithms) are depicted and described as a series of acts. It is to be understood and appreciated that the various embodiments are not limited by the acts illustrated and/or by the order of acts. For example, acts can occur in various orders and/or concurrently, and with other acts not presented or described herein. Furthermore, not all illustrated acts may be required to implement the methods. In addition, the methods could alternatively be represented as a series of interrelated states via a state diagram or events. Additionally, the methods described hereafter are capable of being stored on an article of manufacture (e.g., a machine-readable storage medium) to facilitate transporting and transferring such methodologies to computers. The term article of manufacture, as used herein, is intended to encompass a computer program accessible from any computer-readable device, carrier, or media, including a non-transitory machine-readable storage medium.

It should be noted that although various aspects and embodiments have been described herein in the context of 5G, Universal Mobile Telecommunications System (UMTS), and/or Long Term Evolution (LTE), or other next generation networks, the disclosed aspects are not limited to 5G, a UMTS implementation, and/or an LTE implementation as the techniques can also be applied in 3G, 4G or LTE systems. For example, aspects or features of the disclosed embodiments can be exploited in substantially any wireless communication technology. Such wireless communication technologies can include UMTS, Code Division Multiple Access (CDMA), Wi-Fi, Worldwide Interoperability for Microwave Access (WiMAX), General Packet Radio Service (GPRS), Enhanced GPRS, Third Generation Partnership Project (3GPP), LTE, Third Generation Partnership Project 2 (3GPP2) Ultra Mobile Broadband (UMB), High Speed Packet Access (HSPA), Evolved High Speed Packet Access (HSPA+), High-Speed Downlink Packet Access (HSDPA), High-Speed Uplink Packet Access (HSUPA), Zigbee, or another IEEE 802.XX technology. Additionally, substantially all aspects disclosed herein can be exploited in legacy telecommunication technologies.

Described herein are systems, methods, articles of manufacture, and other embodiments or implementations that can facilitate context-based pre-coding matrix computations for a radio access network for a 5G network. Facilitating context-based pre-coding matrix computations for a radio access network for a 5G network can be implemented in connection with any type of device with a connection to the communications network (e.g., a mobile handset, a computer, a handheld device, etc.) any Internet of things (TOT) device (e.g., toaster, coffee maker, blinds, music players, speakers, etc.), and/or any connected vehicles (cars, airplanes, space rockets, and/or other at least partially automated vehicles (e.g., drones)). In some embodiments the non-limiting term user equipment (UE) is used. It can refer to any type of wireless device that communicates with a radio network node in a cellular or mobile communication system. Examples of UE are target device, device to device (D2D) UE, machine type UE or UE capable of machine to machine (M2M) communication, PDA, Tablet, mobile terminals, smart phone, laptop embedded equipped (LEE), laptop mounted equipment (LME), USB dongles etc. Note that the terms element, elements and antenna ports can be interchangeably used but carry the same meaning in this disclosure. The embodiments are applicable to single carrier as well as to multicarrier (MC) or carrier aggregation (CA) operation of the UE. The term carrier aggregation (CA) is also called (e.g. interchangeably called) “multi-carrier system”, “multi-cell operation”, “multi-carrier operation”, “multi-carrier” transmission and/or reception.

In some embodiments the non-limiting term radio network node or simply network node is used. It can refer to any type of network node that serves UE is connected to other network nodes or network elements or any radio node from where UE receives a signal. Examples of radio network nodes are Node B, base station (BS), multi-standard radio (MSR) node such as MSR BS, eNode B, network controller, radio network controller (RNC), base station controller (BSC), relay, donor node controlling relay, base transceiver station (BTS), access point (AP), transmission points, transmission nodes, RRU, RRH, nodes in distributed antenna system (DAS) etc.

Cloud radio access networks (RAN) can enable the implementation of concepts such as software-defined network (SDN) and network function virtualization (NFV) in 5G networks. This disclosure can facilitate a generic channel state information framework design for a 5G network. Certain embodiments of this disclosure can comprise an SDN controller that can control routing of traffic within the network and between the network and traffic destinations. The SDN controller can be merged with the 5G network architecture to enable service deliveries via open application programming interfaces (“APIs”) and move the network core towards an all internet protocol (“IP”), cloud based, and software driven telecommunications network. The SDN controller can work with, or take the place of policy and charging rules function (“PCRF”) network elements so that policies such as quality of service and traffic management and routing can be synchronized and managed end to end.

To meet the huge demand for data centric applications, 4G standards can be applied 5G, also called new radio (NR) access. 5G networks can comprise the following: data rates of several tens of megabits per second supported for tens of thousands of users; 1 gigabit per second can be offered simultaneously to tens of workers on the same office floor; several hundreds of thousands of simultaneous connections can be supported for massive sensor deployments; spectral efficiency can be enhanced compared to 4G; improved coverage; enhanced signaling efficiency; and reduced latency compared to LTE. In multicarrier system such as OFDM, each subcarrier can occupy bandwidth (e.g., subcarrier spacing). If the carriers use the same bandwidth spacing, then it can be considered a single numerology. However, if the carriers occupy different bandwidth and/or spacing, then it can be considered a multiple numerology.

This disclosure can facilitate precoding matrix (PCM) computations for a large number of antenna arrays available at a service provider's disposal for 5G networks. Utilizing broader knowledge available at a service provider regarding network topology and context, along with granular knowledge regarding available network resources and UE measurements, this disclosure can facilitate gains in power and spectral efficiency and reduction in computation complexity posed by current procedures.

5G networks are expected to integrate almost anything and everything across the globe into the Internet. A host of inter-connected networks including smart cities, vehicular networks, and augmented reality hubs will co-exist within 5G. In terms of technology demands, 5G networks should provide superior speeds over the 4G network. Massive multiple-input multiple-output (MIMO) can sustain the evolution towards 5G because it offers multiple orders of both power and spectral efficiency gains over current LTE technologies. MIMO capable radio units (RUs), with large numbers of antennas, can simultaneously communicate with multiple spatially separated user terminals (UEs) over the same time-frequency resource and exploit multipath propagation. To take advantage of the multiple paths, the precoding matrix (PCM) can be known at the RUs for each UE. Precoding, also known as digital beamforming, can comprise two parts—directivity and power allocation. Both the components can be digitally encoded to the data at the RUs before transmission through antenna arrays.

Currently, pilot signals are used/measured to calculate the precoding matrix. Unlike time division duplex (TDD) modes, in frequency division duplex (FDD) modes, where channel reciprocity cannot be exploited, the existing procedure imposes substantial overhead. Moreover, as MIMO comprises large antenna arrays, the time spent on transmitting pilots can increase and may surpass the coherence time of the channel. The overhead may be of little importance in low mobility scenarios but can become critical in high mobility scenarios where channel coherence time is small. A shorter coherence block of highly mobile users can also imply more frequent computations of precoding matrices, thus increasing the computational complexity at distribute units (DUs) and RUs.

Though conventional techniques can provide a decent array of gains for small MIMO systems, it is not scalable for massive MIMO systems in terms of network overhead and system resources. Thus, a solution can leverage network topology and context knowledge to determine the precoding matrix to be applied at the RU for the UE. An offline precoding prediction model can be generated at a network management platform (e.g., open network automation platform (ONAP), open source management and orchestration MANO, etc.) based on historical data, and/or the UE's activity patterns in terms of mobility and usage. The model can be trained offline at the network management platform and provided to a radio access network intelligent controller (RIC). The RIC-based on network management platform model, can provide instantaneous UE and network resource measurements and a recommendations to the DU regarding the precoding matrix to be used at the RU for the UE. This process can mitigate intercell/intracell interference at the UE. The instantaneous UE measurements (e.g., reference signal received power (RSRP), reference signal received quality (RSRQ) can inform the RIC about the performance experienced by the UE. Network measurements (e.g., spectrum available, antenna power, etc.) can inform the RIC of resources available at the service provider's disposal. The RIC can also have a global view of network resources available at the service provider's disposal at any given time. This information can be further utilized by the RIC to fine-tune the UE's precoding matrix.

A service provider can allocate resources in three dimensions (e.g., space, frequency, and time). Precoding matrices can depend on whether the user or the environment is static or dynamic (e.g., is user or environment moving). The measurements at the UE can comprise how stable the received channels are, and then this information can be returned to the base station. How long this information will last can depend on the covariance time of the channel. For instance if the covariance time of the channel is short, then the channel is changing very fast and the UE has to continuously send precoding matrices. However, if the covariance time is long (e.g., students with mobile devices sitting in a classroom for two hours), then the RIC can pre-compute the precoding matrix and push it to the DU to be used in real-time. Conversely, when a UE is dynamic (e.g., moving on the highway for x amount of time), the RIC can sectorize antenna elements, which are focused on the highway and define the precoding matrix for those antennas.

It should also be noted that an artificial intelligence (AI) component can facilitate automating one or more features in accordance with the disclosed aspects. A memory and a processor as well as other components can include functionality with regard to the figures. The disclosed aspects in connection with generating a precoding matrix can employ various AI-based schemes for carrying out various aspects thereof. For example, a process for detecting one or more trigger events, modifying a precoding matrix as a result of the one or more trigger events, and transmitting the precoding matrix, and so forth, can be facilitated with an example automatic classifier system and process. In another example, a process for penalizing one precoding matrix while preferring another precoding matrix can be facilitated with the example automatic classifier system and process.

An example classifier can be a function that maps an input attribute vector, x=(x1, x2, x3, x4, xn), to a confidence that the input belongs to a class, that is, f(x)=confidence(class). Such classification can employ a probabilistic and/or statistical-based analysis (e.g., factoring into the analysis utilities and costs) to prognose or infer an action that can be automatically performed. In the case of communication systems, for example, attributes can be a signal strength and a technology and the classes can be an output power reduction value. In another example, the attributes can be a signal strength, a technology, and static or dynamic scenarios that can affect output power.

A support vector machine (SVM) is an example of a classifier that can be employed. The SVM can operate by finding a hypersurface in the space of possible inputs, which the hypersurface attempts to split the triggering criteria from the non-triggering events. Intuitively, this makes the classification correct for testing data that is near, but not identical to training data. Other directed and undirected model classification approaches include, for example, naïve Bayes, Bayesian networks, decision trees, neural networks, fuzzy logic models, and probabilistic classification models providing different patterns of independence can be employed. Classification as used herein also may be inclusive of statistical regression that is utilized to develop models of priority.

The disclosed aspects can employ classifiers that are explicitly trained (e.g., via a generic training data) as well as implicitly trained (e.g., via observing mobile device usage as it relates to triggering events, observing network frequency/technology, receiving extrinsic information, and so on). For example, SVMs can be configured via a learning or training phase within a classifier constructor and feature selection module. Thus, the classifier(s) can be used to automatically learn and perform a number of functions, including but not limited to allocating network resources, modifying precoding matrices, and so forth. The criteria can include, but is not limited to, predefined values, frequency attenuation tables or other parameters, service provider preferences and/or policies, and so on.

In one embodiment, described herein is a method comprising receiving, by a wireless network device comprising a processor, model data representative of a precoding prediction model associated with a user equipment device of a wireless network. Based on the precoding prediction model, the method can comprise generating, by the wireless network device, recommendation data representative of a precoding matrix to be sent to a distributed unit device of the wireless network. Additionally, in response to the generating of the recommendation data, the method can comprise sending, by the wireless network device, the recommendation data to the distributed unit device. Furthermore, in response to the sending of the recommendation data, the method can comprise receiving, by the wireless network device, performance data associated with a performance of the user equipment device.

According to another embodiment, a wireless network device can facilitate selecting a precoding prediction model associated with a mobile device of mobile devices of a wireless network. Based on the precoding prediction model, the wireless network device can comprise generating recommendation data representative of a precoding matrix to be sent to a distributed unit device of the wireless network. In response to the generating of the recommendation data, the wireless network device can comprise transmitting the recommendation data to the distributed unit device. Furthermore, in response to the transmitting of the recommendation data, the wireless network device can comprise receiving performance data associated with a performance of the mobile device.

According to yet another embodiment, described herein is a machine-readable storage medium that can perform the operations comprising receiving precoding prediction data representative of a precoding prediction model based on mobile device data representative of mobile device measurements applicable to a mobile device. The machine-readable storage medium can perform the operations comprising generating recommendation data representative of a precoding matrix to be sent to a distributed unit device of a wireless network based on the precoding prediction model. In response to the generating of the recommendation data, the machine-readable storage medium can perform the operations comprising transmitting the recommendation data to the distributed unit device. Additionally, in response to the transmitting of the recommendation data, the machine-readable storage medium can perform the operations comprising receiving performance data associated with a performance of the mobile device.

These and other embodiments or implementations are described in more detail below with reference to the drawings.

Referring now to FIG. 1, illustrated is an example wireless communication system 100 in accordance with various aspects and embodiments of the subject disclosure. In one or more embodiments, system 100 can comprise one or more user equipment UEs 102. The non-limiting term user equipment can refer to any type of device that can communicate with a network node in a cellular or mobile communication system. A UE can have one or more antenna panels having vertical and horizontal elements. Examples of a UE comprise a target device, device to device (D2D) UE, machine type UE or UE capable of machine to machine (M2M) communications, personal digital assistant (PDA), tablet, mobile terminals, smart phone, laptop mounted equipment (LME), universal serial bus (USB) dongles enabled for mobile communications, a computer having mobile capabilities, a mobile device such as cellular phone, a laptop having laptop embedded equipment (LEE, such as a mobile broadband adapter), a tablet computer having a mobile broadband adapter, a wearable device, a virtual reality (VR) device, a heads-up display (HUD) device, a smart car, a machine-type communication (MTC) device, and the like. User equipment UE 102 can also comprise IOT devices that communicate wirelessly.

In various embodiments, system 100 is or comprises a wireless communication network serviced by one or more wireless communication network providers. In example embodiments, a UE 102 can be communicatively coupled to the wireless communication network via a network node 104. The network node (e.g., network node device) can communicate with user equipment (UE), thus providing connectivity between the UE and the wider cellular network. The UE 102 can send transmission type recommendation data to the network node 104. The transmission type recommendation data can comprise a recommendation to transmit data via a closed loop MIMO mode and/or a rank-1 precoder mode.

A network node can have a cabinet and other protected enclosures, an antenna mast, and multiple antennas for performing various transmission operations (e.g., MIMO operations). Network nodes can serve several cells, also called sectors, depending on the configuration and type of antenna. In example embodiments, the UE 102 can send and/or receive communication data via a wireless link to the network node 104. The dashed arrow lines from the network node 104 to the UE 102 represent downlink (DL) communications and the solid arrow lines from the UE 102 to the network nodes 104 represents an uplink (UL) communication.

System 100 can further include one or more communication service provider networks that facilitate providing wireless communication services to various UEs, including UE 102, via the network node 104 and/or various additional network devices (not shown) included in the one or more communication service provider networks. The one or more communication service provider networks can include various types of disparate networks, including but not limited to: cellular networks, femto networks, picocell networks, microcell networks, internet protocol (IP) networks Wi-Fi service networks, broadband service network, enterprise networks, cloud based networks, and the like. For example, in at least one implementation, system 100 can be or include a large scale wireless communication network that spans various geographic areas. According to this implementation, the one or more communication service provider networks can be or include the wireless communication network and/or various additional devices and components of the wireless communication network (e.g., additional network devices and cell, additional UEs, network server devices, etc.). The network node 104 can be connected to the one or more communication service provider networks via one or more backhaul links 108. For example, the one or more backhaul links 108 can comprise wired link components, such as a T1/E1 phone line, a digital subscriber line (DSL) (e.g., either synchronous or asynchronous), an asymmetric DSL (ADSL), an optical fiber backbone, a coaxial cable, and the like. The one or more backhaul links 108 can also include wireless link components, such as but not limited to, line-of-sight (LOS) or non-LOS links which can include terrestrial air-interfaces or deep space links (e.g., satellite communication links for navigation).

Wireless communication system 100 can employ various cellular systems, technologies, and modulation modes to facilitate wireless radio communications between devices (e.g., the UE 102 and the network node 104). While example embodiments might be described for 5G new radio (NR) systems, the embodiments can be applicable to any radio access technology (RAT) or multi-RAT system where the UE operates using multiple carriers e.g. LTE FDD/TDD, GSM/GERAN, CDMA2000 etc.

For example, system 100 can operate in accordance with global system for mobile communications (GSM), universal mobile telecommunications service (UMTS), long term evolution (LTE), LTE frequency division duplexing (LTE FDD, LTE time division duplexing (TDD), high speed packet access (HSPA), code division multiple access (CDMA), wideband CDMA (WCMDA), CDMA2000, time division multiple access (TDMA), frequency division multiple access (FDMA), multi-carrier code division multiple access (MC-CDMA), single-carrier code division multiple access (SC-CDMA), single-carrier FDMA (SC-FDMA), orthogonal frequency division multiplexing (OFDM), discrete Fourier transform spread OFDM (DFT-spread OFDM) single carrier FDMA (SC-FDMA), Filter bank based multi-carrier (FBMC), zero tail DFT-spread-OFDM (ZT DFT-s-OFDM), generalized frequency division multiplexing (GFDM), fixed mobile convergence (FMC), universal fixed mobile convergence (UFMC), unique word OFDM (UW-OFDM), unique word DFT-spread OFDM (UW DFT-Spread-OFDM), cyclic prefix OFDM CP-OFDM, resource-block-filtered OFDM, Wi Fi, WLAN, WiMax, and the like. However, various features and functionalities of system 100 are particularly described wherein the devices (e.g., the UEs 102 and the network node 104) of system 100 are configured to communicate wireless signals using one or more multi carrier modulation schemes, wherein data symbols can be transmitted simultaneously over multiple frequency subcarriers (e.g., OFDM, CP-OFDM, DFT-spread OFMD, UFMC, FMBC, etc.). The embodiments are applicable to single carrier as well as to multicarrier (MC) or carrier aggregation (CA) operation of the UE. The term carrier aggregation (CA) is also called (e.g. interchangeably called) “multi-carrier system”, “multi-cell operation”, “multi-carrier operation”, “multi-carrier” transmission and/or reception. Note that some embodiments are also applicable for Multi RAB (radio bearers) on some carriers (that is data plus speech is simultaneously scheduled).

In various embodiments, system 100 can be configured to provide and employ 5G wireless networking features and functionalities. 5G wireless communication networks are expected to fulfill the demand of exponentially increasing data traffic and to allow people and machines to enjoy gigabit data rates with virtually zero latency. Compared to 4G, 5G supports more diverse traffic scenarios. For example, in addition to the various types of data communication between conventional UEs (e.g., phones, smartphones, tablets, PCs, televisions, Internet enabled televisions, etc.) supported by 4G networks, 5G networks can be employed to support data communication between smart cars in association with driverless car environments, as well as machine type communications (MTCs). Considering the drastic different communication needs of these different traffic scenarios, the ability to dynamically configure waveform parameters based on traffic scenarios while retaining the benefits of multi carrier modulation schemes (e.g., OFDM and related schemes) can provide a significant contribution to the high speed/capacity and low latency demands of 5G networks. With waveforms that split the bandwidth into several sub-bands, different types of services can be accommodated in different sub-bands with the most suitable waveform and numerology, leading to an improved spectrum utilization for 5G networks.

To meet the demand for data centric applications, features of proposed 5G networks may comprise: increased peak bit rate (e.g., 20 Gbps), larger data volume per unit area (e.g., high system spectral efficiency—for example about 3.5 times that of spectral efficiency of long term evolution (LTE) systems), high capacity that allows more device connectivity both concurrently and instantaneously, lower battery/power consumption (which reduces energy and consumption costs), better connectivity regardless of the geographic region in which a user is located, a larger numbers of devices, lower infrastructural development costs, and higher reliability of the communications. Thus, 5G networks may allow for: data rates of several tens of megabits per second should be supported for tens of thousands of users, 1 gigabit per second to be offered simultaneously to tens of workers on the same office floor, for example; several hundreds of thousands of simultaneous connections to be supported for massive sensor deployments; improved coverage, enhanced signaling efficiency; reduced latency compared to LTE.

The upcoming 5G access network may utilize higher frequencies (e.g., >6 GHz) to aid in increasing capacity. Currently, much of the millimeter wave (mmWave) spectrum, the band of spectrum between 30 gigahertz (Ghz) and 300 Ghz is underutilized. The millimeter waves have shorter wavelengths that range from 10 millimeters to 1 millimeter, and these mmWave signals experience severe path loss, penetration loss, and fading. However, the shorter wavelength at mmWave frequencies also allows more antennas to be packed in the same physical dimension, which allows for large-scale spatial multiplexing and highly directional beamforming.

Performance can be improved if both the transmitter and the receiver are equipped with multiple antennas. Multi-antenna techniques can significantly increase the data rates and reliability of a wireless communication system. The use of multiple input multiple output (MIMO) techniques, which was introduced in the third-generation partnership project (3GPP) and has been in use (including with LTE), is a multi-antenna technique that can improve the spectral efficiency of transmissions, thereby significantly boosting the overall data carrying capacity of wireless systems. The use of multiple-input multiple-output (MIMO) techniques can improve mmWave communications, and has been widely recognized a potentially important component for access networks operating in higher frequencies. MIMO can be used for achieving diversity gain, spatial multiplexing gain and beamforming gain. For these reasons, MIMO systems are an important part of the 3rd and 4th generation wireless systems, and are planned for use in 5G systems.

Referring now to FIG. 2, illustrated is an example schematic system block diagram of a radio access network intelligent controller according to one or more embodiments.

In the embodiment shown in FIG. 2, a RIC 200 can comprise sub-components (e.g., prediction component 202, analysis component 204, AI component 206, and recommendation component 208), processor 210 and memory 212 can bi-directionally communicate with each other. It should also be noted that in alternative embodiments that other components including, but not limited to the sub-components, processor 210, and/or memory 212, can be external to the RIC 200. Aspects of the processor 210 can constitute machine-executable component(s) embodied within machine(s), e.g., embodied in one or more computer readable mediums (or media) associated with one or more machines. Such component(s), when executed by the one or more machines, e.g., computer(s), computing device(s), virtual machine(s), etc. can cause the machine(s) to perform the operations described by the RIC 200. In an aspect, the RIC 200 can also include memory 212 that stores computer executable components and instructions.

The analysis component 204 can be configured to receive UE measurement and/or network condition data from a DU (e.g., historical data, and/or the UE's activity patterns in terms of mobility and usage). The analysis component 204 can also be configured to receive pattern detection and model development data, from a network management platform, that has been trained offline at the network management platform. Consequently, based on network topology and context knowledge, the analysis component 204 can analyze the aforementioned data to facilitate a precoding matrix prediction. The prediction component 202 can then leverage the analysis data to generate a prediction of the precoding matrix to be applied at the RU for the UE 102. The recommendation component 208, can then facilitate providing a recommendation to the DU, wherein the recommendation comprises data on the predicted precoding matrix to be used by the RU. In additional, or alternative embodiments, the RIC 200 can also comprise the AI component 206 that can be configured to learn from previous patterns associated with the UE 102 and the network, previous data received from and/or sent to the DU, etc. Consequently, the AI component 206 can generate prediction data that can influence the recommended precoding matrix.

Referring now to FIG. 3, illustrated is an example schematic system block diagram of a context-based precoding matrix system according to one or more embodiments.

Within a closed-loop system network management platform 300 (e.g., open network automation platform (ONAP) open source management and orchestration (MANO), etc.), an offline precoding prediction model can be generated based on historical data, and/or the UE's activity patterns in terms of mobility and usage. The model can be trained offline at the network management platform and provided to the RIC 200.

A historical database 302 can send and receive historical data, associated with UEs 102, 104, to block 304 where the UE data can be collected and/or correlated by a collection and correlation component of the network management platform. For example, location data can be correlated to time data associated with a specific UE (e.g., UE 102 is static for two hours). The UE data can comprise UE collection data, UE correlation data, UE usage data, UE device type data, etc. The UE data can be sent to the UE data collection and correlation component at block 504 from a network conditions and UE measurement component within the RIC at block 308. Once the UE data collection and correlation component receives the UE data and correlates the UE data, the UE data collection and correlation component can send the UE data and correlation data to a learning component at block 306. The learning component can utilize AI or machine learning (ML) to detect UE mobility patterns that can then be sent to a precoding matrix recommendation component (e.g., 208) of the RIC 200 to facilitate a selection of a precoder matrix at the RU 316. The RIC 200 can provide instantaneous UE and network resource measurements and recommendations to the DU regarding the precoding matrix to be used at the RU 316 for the UE 102.

The network conditions and UE measurement component of the distributed units (DUs) at block 312 can send the network condition and measurement data to the network conditions and UE measurement block 308 (e.g., analysis component 204) within the RIC 200. The RIC 200 can then use the network measurements to predict a precoder matrix and to recommend (e.g., recommendation component 208) the precoder matrix (e.g., prediction component 202) at block 310. Thereafter, the precoder matrix prediction can be sent to the DU as precoder 1 at block 314 and then be sent to the RU 316. Because this is a closed-loop system, it can be further conditioned and fine-tuned via the learning component the more data is curated.

Referring now to FIG. 4, illustrated is an example schematic system block diagram of a context-based precoding matrix system for UEs with a larger coherence time according to one or more embodiments. FIG. 4, depicts a scenario for UEs 102 with a larger coherence time. The RIC 200 can obtain channel state information through measurements on uplink pilots (TDD) or feedback from the UE (FDD). The RIC can then perform multi-user (MU)-MIMO precoding calculations across neighboring RUs 316 for multiple-users to generate the precoder 1 314 matrix. This approach can avoid and/or minimize spatial cross-talk (e.g., intracellular interference) in a MIMO channel with MIMO channels being transformed into a set of independent parallel “virtual” channels.

Referring now to FIG. 5 illustrates an example schematic system block diagram of a context-based precoding matrix system comprising a reduced number of channels for UEs according to one or more embodiments. For UEs 102 with a shorter coherence time (e.g., highly mobile UEs), an instantaneous feedback on a reduced number of channels is depicted in FIG. 5. The instantaneous feedback on the reduced number of channels can better serve service level agreements (SLAs) to highly mobile UEs at reduced complexity in the DU. The two-stage digital precoding technique in FIG. 5 can exploit topology and context information to partition UEs into different groups. For example, each group can comprise UEs with approximately the same channel covariance eigenspace, such that the covariance eigenspaces of different UE groups are near-orthogonal to each other. Once the UE 102 is identified to belong to a certain group, a precoding matrix, (e.g., precoder I 314) can be recommended by the RIC 200, accordingly. Precoder II 502 can be fed back on reduced channels from the UE 102. This multi-step technique approach can result in low overhead in precoding calculations, by reducing pilot signaling and channel feedback. Thus, the precoder II 502 can be fed back as a part of closed loop feedback to the precoding matrix computation (e.g., precoder I 314) at the RIC 200.

The precoder 1 314 matrix can come from the RIC 200, while the precoder II matrix can come from instantaneous channel feedback so that the UE 102 does not have to sense every channel. Thus, the RIC 200 can calculate the precoding matrices for a few of the antennas, whereas the other antenna element feedback can come from the UE 102, in real-time, to optimize the precoder 1 314 and ensure that the UE 102 receives a good channel. Since all channels do not need to be measured, some can be measured by the RIC 200, and some can be measured by the UE 102, which can reduce calculations at the DU in real-time. Thus, the uplink bandwith to send the measurements can be reduced.

Referring now to FIG. 6, illustrated is an example flow diagram for a method for a context-based precoding matrix for a 5G network according to one or more embodiments. At element 600, a method can comprise receiving (e.g., via the RIC 200) model data representative of a precoding prediction model associated with a user equipment device of a wireless network. Based on the precoding prediction model, at element 602, the method can comprise generating (e.g., via the RIC 200) recommendation data representative of a precoding matrix to be sent to a distributed unit device of the wireless network. Additionally, in response to the generating of the recommendation data, the method can comprise sending (e.g., via the RIC 200) the recommendation data to the distributed unit device at element 604. Furthermore, in response to the sending of the recommendation data, at element 606, the method can comprise receiving (e.g., via the RIC 200) performance data associated with a performance of the user equipment device (e.g., the UE 102).

Referring now to FIG. 7, illustrated is an example flow diagram for a wireless network device for a context-based precoding matrix for a 5G network according to one or more embodiments. At element 700, a wireless network device (e.g., via the RIC 200) can faciliate selecting a precoding prediction model associated with a UE 102 of mobile devices of a wireless network. Based on the precoding prediction model, the wireless network device (e.g., via the RIC 200) can comprise generating recommendation data representative of a precoding matrix to be sent to a distributed unit device of the wireless network. In response to the generating of the recommendation data, the wireless network device (e.g., via the RIC 200) can facilitate transmitting the recommendation data to the distributed unit device. Furthermore, in response to the transmitting of the recommendation data, the wireless network device (e.g., via the RIC 200) can facilitate receiving performance data associated with a performance of the mobile device.

Referring now to FIG. 8, illustrated is an example flow diagram for a machine-readable medium for a context-based precoding matrix for a 5G network according to one or more embodiments. According to yet another embodiment, described herein is a machine-readable storage medium that can perform the operations comprising receiving precoding prediction data (e.g., via the RIC 200) representative of a precoding prediction model based on mobile device data representative of mobile device measurements applicable to a UE 102. The machine-readable storage medium can perform the operations comprising generating recommendation data (e.g., via the RIC 200) representative of a precoding matrix to be sent to a distributed unit device of a wireless network based on the precoding prediction model. In response to the generating of the recommendation data, the machine-readable storage medium can facilitate performance of the operations comprising transmitting (e.g., via the RIC 200) the recommendation data to the distributed unit device. Additionally, in response to the transmitting of the recommendation data, the machine-readable storage medium can perform the operations comprising receiving (e.g., via the RIC 200) performance data associated with a performance of the UE 102.

Referring now to FIG. 9, illustrated is an example block diagram of an example mobile handset 900 operable to engage in a system architecture that facilitates wireless communications according to one or more embodiments described herein. Although a mobile handset is illustrated herein, it will be understood that other devices can be a mobile device, and that the mobile handset is merely illustrated to provide context for the embodiments of the various embodiments described herein. The following discussion is intended to provide a brief, general description of an example of a suitable environment in which the various embodiments can be implemented. While the description includes a general context of computer-executable instructions embodied on a machine-readable storage medium, those skilled in the art will recognize that the innovation also can be implemented in combination with other program modules and/or as a combination of hardware and software.

Generally, applications (e.g., program modules) can include routines, programs, components, data structures, etc., that perform particular tasks or implement particular abstract data types. Moreover, those skilled in the art will appreciate that the methods described herein can be practiced with other system configurations, including single-processor or multiprocessor systems, minicomputers, mainframe computers, as well as personal computers, hand-held computing devices, microprocessor-based or programmable consumer electronics, and the like, each of which can be operatively coupled to one or more associated devices.

A computing device can typically include a variety of machine-readable media. Machine-readable media can be any available media that can be accessed by the computer and includes both volatile and non-volatile media, removable and non-removable media. By way of example and not limitation, computer-readable media can comprise computer storage media and communication media. Computer storage media can include volatile and/or non-volatile media, removable and/or non-removable media implemented in any method or technology for storage of information, such as computer-readable instructions, data structures, program modules, or other data. Computer storage media can include, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD ROM, digital video disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the computer.

Communication media typically embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of the any of the above should also be included within the scope of computer-readable media.

The handset includes a processor 902 for controlling and processing all onboard operations and functions. A memory 904 interfaces to the processor 902 for storage of data and one or more applications 906 (e.g., a video player software, user feedback component software, etc.). Other applications can include voice recognition of predetermined voice commands that facilitate initiation of the user feedback signals. The applications 906 can be stored in the memory 904 and/or in a firmware 908, and executed by the processor 902 from either or both the memory 904 or/and the firmware 908. The firmware 908 can also store startup code for execution in initializing the handset 900. A communications component 910 interfaces to the processor 902 to facilitate wired/wireless communication with external systems, e.g., cellular networks, VoIP networks, and so on. Here, the communications component 910 can also include a suitable cellular transceiver 911 (e.g., a GSM transceiver) and/or an unlicensed transceiver 913 (e.g., Wi-Fi, WiMax) for corresponding signal communications. The handset 900 can be a device such as a cellular telephone, a PDA with mobile communications capabilities, and messaging-centric devices. The communications component 910 also facilitates communications reception from terrestrial radio networks (e.g., broadcast), digital satellite radio networks, and Internet-based radio services networks.

The handset 900 includes a display 912 for displaying text, images, video, telephony functions (e.g., a Caller ID function), setup functions, and for user input. For example, the display 912 can also be referred to as a “screen” that can accommodate the presentation of multimedia content (e.g., music metadata, messages, wallpaper, graphics, etc.). The display 912 can also display videos and can facilitate the generation, editing and sharing of video quotes. A serial I/O interface 914 is provided in communication with the processor 902 to facilitate wired and/or wireless serial communications (e.g., USB, and/or IEEE 1394) through a hardwire connection, and other serial input devices (e.g., a keyboard, keypad, and mouse). This can support updating and troubleshooting the handset 900, for example. Audio capabilities are provided with an audio I/O component 916, which can include a speaker for the output of audio signals related to, for example, indication that the user pressed the proper key or key combination to initiate the user feedback signal. The audio I/O component 916 also facilitates the input of audio signals through a microphone to record data and/or telephony voice data, and for inputting voice signals for telephone conversations.

The handset 900 can include a slot interface 918 for accommodating a SIC (Subscriber Identity Component) in the form factor of a card Subscriber Identity Module (SIM) or universal SIM 920, and interfacing the SIM card 920 with the processor 902. However, it is to be appreciated that the SIM card 920 can be manufactured into the handset 900, and updated by downloading data and software.

The handset 900 can process IP data traffic through the communications component 910 to accommodate IP traffic from an IP network such as, for example, the Internet, a corporate intranet, a home network, a person area network, etc., through an ISP or broadband cable provider. Thus, VoIP traffic can be utilized by the handset 900 and IP-based multimedia content can be received in either an encoded or decoded format.

A video processing component 922 (e.g., a camera) can be provided for decoding encoded multimedia content. The video processing component 922 can aid in facilitating the generation, editing, and sharing of video quotes. The handset 900 also includes a power source 924 in the form of batteries and/or an AC power subsystem, which power source 924 can interface to an external power system or charging equipment (not shown) by a power I/O component 926.

The handset 900 can also include a video component 930 for processing video content received and, for recording and transmitting video content. For example, the video component 930 can facilitate the generation, editing and sharing of video quotes. A location tracking component 932 facilitates geographically locating the handset 900. As described hereinabove, this can occur when the user initiates the feedback signal automatically or manually. A user input component 934 facilitates the user initiating the quality feedback signal. The user input component 934 can also facilitate the generation, editing and sharing of video quotes. The user input component 934 can include such conventional input device technologies such as a keypad, keyboard, mouse, stylus pen, and/or touchscreen, for example.

Referring again to the applications 906, a hysteresis component 936 facilitates the analysis and processing of hysteresis data, which is utilized to determine when to associate with the access point. A software trigger component 938 can be provided that facilitates triggering of the hysteresis component 936 when the Wi-Fi transceiver 913 detects the beacon of the access point. A SIP client 940 enables the handset 900 to support SIP protocols and register the subscriber with the SIP registrar server. The applications 906 can also include a client 942 that provides at least the capability of discovery, play and store of multimedia content, for example, music.

The handset 900, as indicated above related to the communications component 910, includes an indoor network radio transceiver 913 (e.g., Wi-Fi transceiver). This function supports the indoor radio link, such as IEEE 802.11, for the dual-mode GSM handset 900. The handset 900 can accommodate at least satellite radio services through a handset that can combine wireless voice and digital radio chipsets into a single handheld device.

Referring now to FIG. 10, illustrated is an example block diagram of an example computer 1000 operable to engage in a system architecture that facilitates wireless communications according to one or more embodiments described herein. The computer 1000 can provide networking and communication capabilities between a wired or wireless communication network and a server (e.g., Microsoft server) and/or communication device. In order to provide additional context for various aspects thereof, FIG. 10 and the following discussion are intended to provide a brief, general description of a suitable computing environment in which the various aspects of the innovation can be implemented to facilitate the establishment of a transaction between an entity and a third party. While the description above is in the general context of computer-executable instructions that can run on one or more computers, those skilled in the art will recognize that the innovation also can be implemented in combination with other program modules and/or as a combination of hardware and software.

Generally, program modules include routines, programs, components, data structures, etc., that perform particular tasks or implement particular abstract data types. Moreover, those skilled in the art will appreciate that the inventive methods can be practiced with other computer system configurations, including single-processor or multiprocessor computer systems, minicomputers, mainframe computers, as well as personal computers, hand-held computing devices, microprocessor-based or programmable consumer electronics, and the like, each of which can be operatively coupled to one or more associated devices.

The illustrated aspects of the innovation can also be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located in both local and remote memory storage devices.

Computing devices typically include a variety of media, which can include computer-readable storage media or communications media, which two terms are used herein differently from one another as follows.

Computer-readable storage media can be any available storage media that can be accessed by the computer and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer-readable storage media can be implemented in connection with any method or technology for storage of information such as computer-readable instructions, program modules, structured data, or unstructured data. Computer-readable storage media can include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or other tangible and/or non-transitory media which can be used to store desired information. Computer-readable storage media can be accessed by one or more local or remote computing devices, e.g., via access requests, queries or other data retrieval protocols, for a variety of operations with respect to the information stored by the medium.

Communications media can embody computer-readable instructions, data structures, program modules or other structured or unstructured data in a data signal such as a modulated data signal, e.g., a carrier wave or other transport mechanism, and includes any information delivery or transport media. The term “modulated data signal” or signals refers to a signal that has one or more of its characteristics set or changed in such a manner as to encode information in one or more signals. By way of example, and not limitation, communication media include wired media, such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media.

With reference to FIG. 10, implementing various aspects described herein with regards to the end-user device can include a computer 1000, the computer 1000 including a processing unit 1004, a system memory 1006 and a system bus 1008. The system bus 1008 couples system components including, but not limited to, the system memory 1006 to the processing unit 1004. The processing unit 1004 can be any of various commercially available processors. Dual microprocessors and other multi-processor architectures can also be employed as the processing unit 1004.

The system bus 1008 can be any of several types of bus structure that can further interconnect to a memory bus (with or without a memory controller), a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. The system memory 1006 includes read-only memory (ROM) 1027 and random access memory (RAM) 1012. A basic input/output system (BIOS) is stored in a non-volatile memory 1027 such as ROM, EPROM, EEPROM, which BIOS contains the basic routines that help to transfer information between elements within the computer 1000, such as during start-up. The RAM 1012 can also include a high-speed RAM such as static RAM for caching data.

The computer 1000 further includes an internal hard disk drive (HDD) 1014 (e.g., EIDE, SATA), which internal hard disk drive 1014 can also be configured for external use in a suitable chassis (not shown), a magnetic floppy disk drive (FDD) 1016, (e.g., to read from or write to a removable diskette 1018) and an optical disk drive 1020, (e.g., reading a CD-ROM disk 1022 or, to read from or write to other high capacity optical media such as the DVD). The hard disk drive 1014, magnetic disk drive 1016 and optical disk drive 1020 can be connected to the system bus 1008 by a hard disk drive interface 1024, a magnetic disk drive interface 1026 and an optical drive interface 1028, respectively. The interface 1024 for external drive implementations includes at least one or both of Universal Serial Bus (USB) and IEEE 1394 interface technologies. Other external drive connection technologies are within contemplation of the subject innovation.

The drives and their associated computer-readable media provide nonvolatile storage of data, data structures, computer-executable instructions, and so forth. For the computer 1000 the drives and media accommodate the storage of any data in a suitable digital format. Although the description of computer-readable media above refers to a HDD, a removable magnetic diskette, and a removable optical media such as a CD or DVD, it should be appreciated by those skilled in the art that other types of media which are readable by a computer 1000, such as zip drives, magnetic cassettes, flash memory cards, cartridges, and the like, can also be used in the exemplary operating environment, and further, that any such media can contain computer-executable instructions for performing the methods of the disclosed innovation.

A number of program modules can be stored in the drives and RAM 1012, including an operating system 1030, one or more application programs 1032, other program modules 1034 and program data 1036. All or portions of the operating system, applications, modules, and/or data can also be cached in the RAM 1012. It is to be appreciated that the innovation can be implemented with various commercially available operating systems or combinations of operating systems.

A user can enter commands and information into the computer 1000 through one or more wired/wireless input devices, e.g., a keyboard 1038 and a pointing device, such as a mouse 1040. Other input devices (not shown) can include a microphone, an IR remote control, a joystick, a game pad, a stylus pen, touchscreen, or the like. These and other input devices are often connected to the processing unit 1004 through an input device interface 1042 that is coupled to the system bus 1008, but can be connected by other interfaces, such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, etc.

A monitor 1044 or other type of display device is also connected to the system bus 1008 through an interface, such as a video adapter 1046. In addition to the monitor 1044, a computer 1000 typically includes other peripheral output devices (not shown), such as speakers, printers, etc.

The computer 1000 can operate in a networked environment using logical connections by wired and/or wireless communications to one or more remote computers, such as a remote computer(s) 1048. The remote computer(s) 1048 can be a workstation, a server computer, a router, a personal computer, portable computer, microprocessor-based entertainment device, a peer device or other common network node, and typically includes many or all of the elements described relative to the computer, although, for purposes of brevity, only a memory/storage device 1050 is illustrated. The logical connections depicted include wired/wireless connectivity to a local area network (LAN) 1052 and/or larger networks, e.g., a wide area network (WAN) 1054. Such LAN and WAN networking environments are commonplace in offices and companies, and facilitate enterprise-wide computer networks, such as intranets, all of which can connect to a global communications network, e.g., the Internet.

When used in a LAN networking environment, the computer 1000 is connected to the local network 1052 through a wired and/or wireless communication network interface or adapter 1056. The adapter 1056 can facilitate wired or wireless communication to the LAN 1052, which can also include a wireless access point disposed thereon for communicating with the wireless adapter 1056.

When used in a WAN networking environment, the computer 1000 can include a modem 1058, or is connected to a communications server on the WAN 1054, or has other means for establishing communications over the WAN 1054, such as by way of the Internet. The modem 1058, which can be internal or external and a wired or wireless device, is connected to the system bus 1008 through the input device interface 1042. In a networked environment, program modules depicted relative to the computer, or portions thereof, can be stored in the remote memory/storage device 1050. It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers can be used.

The computer is operable to communicate with any wireless devices or entities operatively disposed in wireless communication, e.g., a printer, scanner, desktop and/or portable computer, portable data assistant, communications satellite, any piece of equipment or location associated with a wirelessly detectable tag (e.g., a kiosk, news stand, restroom), and telephone. This includes at least Wi-Fi and Bluetooth™ wireless technologies. Thus, the communication can be a predefined structure as with a conventional network or simply an ad hoc communication between at least two devices.

Wi-Fi, or Wireless Fidelity, allows connection to the Internet from a couch at home, in a hotel room, or a conference room at work, without wires. Wi-Fi is a wireless technology similar to that used in a cell phone that enables such devices, e.g., computers, to send and receive data indoors and out; anywhere within the range of a base station. Wi-Fi networks use radio technologies called IEEE 802.11 (a, b, g, etc.) to provide secure, reliable, fast wireless connectivity. A Wi-Fi network can be used to connect computers to each other, to the Internet, and to wired networks (which use IEEE 802.3 or Ethernet). Wi-Fi networks operate in the unlicensed 2.4 and 5 GHz radio bands, at an 7 Mbps (802.11a) or 54 Mbps (802.11b) data rate, for example, or with products that contain both bands (dual band), so the networks can provide real-world performance similar to the basic 16BaseT wired Ethernet networks used in many offices.

An aspect of 5G, which differentiates from previous 4G systems, is the use of NR. NR architecture can be designed to support multiple deployment cases for independent configuration of resources used for RACH procedures. Since the NR can provide additional services than those provided by LTE, efficiencies can be generated by leveraging the pros and cons of LTE and NR to facilitate the interplay between LTE and NR, as discussed herein.

Reference throughout this specification to “one embodiment,” or “an embodiment,” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, the appearances of the phrase “in one embodiment,” “in one aspect,” or “in an embodiment,” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics can be combined in any suitable manner in one or more embodiments.

As used in this disclosure, in some embodiments, the terms “component,” “system,” “interface,” and the like are intended to refer to, or comprise, a computer-related entity or an entity related to an operational apparatus with one or more specific functionalities, wherein the entity can be either hardware, a combination of hardware and software, software, or software in execution, and/or firmware. As an example, a component can be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, computer-executable instructions, a program, and/or a computer. By way of illustration and not limitation, both an application running on a server and the server can be a component.

One or more components can reside within a process and/or thread of execution and a component can be localized on one computer and/or distributed between two or more computers. In addition, these components can execute from various computer readable media having various data structures stored thereon. The components can communicate via local and/or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and/or across a network such as the Internet with other systems via the signal). As another example, a component can be an apparatus with specific functionality provided by mechanical parts operated by electric or electronic circuitry, which is operated by a software application or firmware application executed by one or more processors, wherein the processor can be internal or external to the apparatus and can execute at least a part of the software or firmware application. As yet another example, a component can be an apparatus that provides specific functionality through electronic components without mechanical parts, the electronic components can comprise a processor therein to execute software or firmware that confer(s) at least in part the functionality of the electronic components. In an aspect, a component can emulate an electronic component via a virtual machine, e.g., within a cloud computing system. While various components have been illustrated as separate components, it will be appreciated that multiple components can be implemented as a single component, or a single component can be implemented as multiple components, without departing from example embodiments.

In addition, the words “example” and “exemplary” are used herein to mean serving as an instance or illustration. Any embodiment or design described herein as “example” or “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word example or exemplary is intended to present concepts in a concrete fashion. As used in this application, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or.” That is, unless specified otherwise or clear from context, “X employs A or B” is intended to mean any of the natural inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, then “X employs A or B” is satisfied under any of the foregoing instances. In addition, the articles “a” and “an” as used in this application and the appended claims should generally be construed to mean “one or more” unless specified otherwise or clear from context to be directed to a singular form.

Moreover, terms such as “mobile device equipment,” “mobile station,” “mobile,” subscriber station,” “access terminal,” “terminal,” “handset,” “communication device,” “mobile device” (and/or terms representing similar terminology) can refer to a wireless device utilized by a subscriber or mobile device of a wireless communication service to receive or convey data, control, voice, video, sound, gaming or substantially any data-stream or signaling-stream. The foregoing terms are utilized interchangeably herein and with reference to the related drawings. Likewise, the terms “access point (AP),” “Base Station (BS),” BS transceiver, BS device, cell site, cell site device, “Node B (NB),” “evolved Node B (eNode B),” “home Node B (HNB)” and the like, are utilized interchangeably in the application, and refer to a wireless network component or appliance that transmits and/or receives data, control, voice, video, sound, gaming or substantially any data-stream or signaling-stream from one or more subscriber stations. Data and signaling streams can be packetized or frame-based flows.

Furthermore, the terms “device,” “communication device,” “mobile device,” “subscriber,” “customer entity,” “consumer,” “customer entity,” “entity” and the like are employed interchangeably throughout, unless context warrants particular distinctions among the terms. It should be appreciated that such terms can refer to human entities or automated components supported through artificial intelligence (e.g., a capacity to make inference based on complex mathematical formalisms), which can provide simulated vision, sound recognition and so forth.

Embodiments described herein can be exploited in substantially any wireless communication technology, comprising, but not limited to, wireless fidelity (Wi-Fi), global system for mobile communications (GSM), universal mobile telecommunications system (UMTS), worldwide interoperability for microwave access (WiMAX), enhanced general packet radio service (enhanced GPRS), third generation partnership project (3GPP) long term evolution (LTE), third generation partnership project 2 (3GPP2) ultra mobile broadband (UMB), high speed packet access (HSPA), Z-Wave, Zigbee and other 802.XX wireless technologies and/or legacy telecommunication technologies.

The various aspects described herein can relate to New Radio (NR), which can be deployed as a standalone radio access technology or as a non-standalone radio access technology assisted by another radio access technology, such as Long Term Evolution (LTE), for example. It should be noted that although various aspects and embodiments have been described herein in the context of 5G, Universal Mobile Telecommunications System (UMTS), and/or Long Term Evolution (LTE), or other next generation networks, the disclosed aspects are not limited to 5G, a UMTS implementation, and/or an LTE implementation as the techniques can also be applied in 3G, 4G, or LTE systems. For example, aspects or features of the disclosed embodiments can be exploited in substantially any wireless communication technology. Such wireless communication technologies can include UMTS, Code Division Multiple Access (CDMA), Wi-Fi, Worldwide Interoperability for Microwave Access (WiMAX), General Packet Radio Service (GPRS), Enhanced GPRS, Third Generation Partnership Project (3GPP), LTE, Third Generation Partnership Project 2 (3GPP2) Ultra Mobile Broadband (UMB), High Speed Packet Access (HSPA), Evolved High Speed Packet Access (HSPA+), High-Speed Downlink Packet Access (HSDPA), High-Speed Uplink Packet Access (HSUPA), Zigbee, or another IEEE 802.XX technology. Additionally, substantially all aspects disclosed herein can be exploited in legacy telecommunication technologies.

As used herein, the term “infer” or “inference” refers generally to the process of reasoning about, or inferring states of, the system, environment, user, and/or intent from a set of observations as captured via events and/or data. Captured data and events can include user data, device data, environment data, data from sensors, sensor data, application data, implicit data, explicit data, etc. Inference can be employed to identify a specific context or action, or can generate a probability distribution over states of interest based on a consideration of data and events, for example.

Inference can also refer to techniques employed for composing higher-level events from a set of events and/or data. Such inference results in the construction of new events or actions from a set of observed events and/or stored event data, whether the events are correlated in close temporal proximity, and whether the events and data come from one or several event and data sources. Various classification procedures and/or systems (e.g., support vector machines, neural networks, expert systems, Bayesian belief networks, fuzzy logic, and data fusion engines) can be employed in connection with performing automatic and/or inferred action in connection with the disclosed subject matter.

In addition, the various embodiments can be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques to produce software, firmware, hardware, or any combination thereof to control a computer to implement the disclosed subject matter. The term “article of manufacture” as used herein is intended to encompass a computer program accessible from any computer-readable device, machine-readable device, computer-readable carrier, computer-readable media, machine-readable media, computer-readable (or machine-readable) storage/communication media. For example, computer-readable media can comprise, but are not limited to, a magnetic storage device, e.g., hard disk; floppy disk; magnetic strip(s); an optical disk (e.g., compact disk (CD), a digital video disc (DVD), a Blu-ray Disc™ (BD)); a smart card; a flash memory device (e.g., card, stick, key drive); and/or a virtual device that emulates a storage device and/or any of the above computer-readable media. Of course, those skilled in the art will recognize many modifications can be made to this configuration without departing from the scope or spirit of the various embodiments.

The above description of illustrated embodiments of the subject disclosure, including what is described in the Abstract, is not intended to be exhaustive or to limit the disclosed embodiments to the precise forms disclosed. While specific embodiments and examples are described herein for illustrative purposes, various modifications are possible that are considered within the scope of such embodiments and examples, as those skilled in the relevant art can recognize.

In this regard, while the subject matter has been described herein in connection with various embodiments and corresponding figures, where applicable, it is to be understood that other similar embodiments can be used or modifications and additions can be made to the described embodiments for performing the same, similar, alternative, or substitute function of the disclosed subject matter without deviating therefrom. Therefore, the disclosed subject matter should not be limited to any single embodiment described herein, but rather should be construed in breadth and scope in accordance with the appended claims below. 

1. A method, comprising: receiving, by a wireless network device comprising a processor, model data representative of a precoding prediction model associated with a user equipment device of a wireless network; based on the precoding prediction model, generating, by the wireless network device, recommendation data representative of a precoding matrix to be sent to a distributed unit device of the wireless network; in response to the generating of the recommendation data, sending, by the wireless network device, the recommendation data to the distributed unit device; in response to the sending of the recommendation data, receiving, by the wireless network device, performance data associated with a performance of the user equipment device; performing multiple-input multiple-output precoding determinations associated with neighboring remote unit devices to reduce intracellular interference between user equipment devices of the wireless network; and based on a result of the performing the multiple-input multiple-output precoding determinations, combining multiple-input multiple-output channels currently configured for use by the user equipment devices to reduce a number of the multiple-input multiple-output channels, resulting in a reduced number of multiple-input multiple-output channels.
 2. The method of claim 1, further comprising: receiving, by the wireless network device, resource data representative of available resources of the wireless network.
 3. The method of claim 2, further comprising: in response to the receiving the resource data, modifying, by the wireless network device, the precoding matrix.
 4. The method of claim 1, wherein the precoding prediction model is based on historical data associated with usage of the user equipment device.
 5. The method of claim 1, comprising: receiving, by the wireless network device from the user equipment device, channel state information associated with a frequency division duplex.
 6. The method of claim 1, wherein the performance data comprises spectrum data associated with a spectrum of the wireless network utilized by the user equipment device.
 7. The method of claim 1, wherein the performance data comprises power data associated with a power of an antenna of the wireless network with which the user equipment device communicates.
 8. A wireless network device, comprising: a processor; and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising: selecting a precoding prediction model associated with a mobile device of mobile devices of a wireless network; based on the precoding prediction model, generating recommendation data representative of a precoding matrix to be sent to a distributed unit device of the wireless network; in response to the generating of the recommendation data, transmitting the recommendation data to the distributed unit device; in response to the transmitting of the recommendation data, receiving performance data associated with a performance of the mobile device; determining multiple-input multiple-output precoding information associated with neighboring remote unit devices to minimize intracellular interference between the mobile devices; and based on a result of the performing the multiple-input multiple-output precoding calculations, combining multiple-input multiple-output channels currently configured for use by the mobile devices to reduce a number of the multiple-input multiple-output channels, resulting in a reduced number of multiple-input multiple-output channels.
 9. The wireless network device of claim 8, wherein the precoding prediction model is based on historical data associated with usage of the mobile device.
 10. The wireless network device of claim 8, wherein the precoding prediction model is based on a detected pattern associated with the mobile device.
 11. The wireless network device of claim 10, wherein the reduced number of the multiple-input multiple-output channels are in accordance with a service level agreement of the wireless network.
 12. The wireless network device of claim 8, wherein the operations further comprise: based on a covariance eigenspace of a channel usable by the mobile devices for communication with the wireless network, partitioning the mobile devices into groups of mobile devices.
 13. The wireless network device of claim 12, wherein, as a result of the partitioning, covariance eigenspaces of the groups are orthogonal.
 14. The wireless network device of claim 12, wherein the precoding matrix is a first precoding matrix, and wherein the operations further comprise: based on the mobile device being determined to belong to a group of the groups, recommending a second precoding matrix to the distributed unit device.
 15. A non-transitory machine-readable medium, comprising executable instructions that, when executed by a processor, facilitate performance of operations, comprising: based on mobile device data representative of mobile device measurements applicable to a mobile device, receiving precoding prediction data representative of a precoding prediction model; based on the precoding prediction model, generating recommendation data representative of a precoding matrix to be sent to a distributed unit device of a wireless network; in response to the generating of the recommendation data, transmitting the recommendation data to the distributed unit device; in response to the transmitting of the recommendation data, receiving performance data associated with a performance of the mobile device; performing multiple-input multiple-output precoding calculations associated with neighboring remote unit devices to minimize intracellular interference between mobile devices of the wireless network; and based on a result of the performing the multiple-input multiple-output precoding calculations, combining multiple-input multiple-output channels currently configured for use by the mobile devices to reduce a number of the multiple-input multiple-output channels, resulting in a reduced number of multiple-input multiple-output channels.
 16. The non-transitory machine-readable medium of claim 15, wherein the mobile device data comprises location data associated with a previous location of the mobile device.
 17. The non-transitory machine-readable medium of claim 15, wherein the mobile device data comprises usage data representative of a previous usage pattern of the mobile device.
 18. The non-transitory machine-readable medium of claim 15, wherein the mobile device is a first mobile device, and wherein the mobile device data comprises co-located mobile device data representative of a second mobile device determined to have been co-located to the first mobile device.
 19. The non-transitory machine-readable medium of claim 15, wherein the operations further comprise: detecting a pattern associated with the mobile device data, resulting in a detected pattern.
 20. The non-transitory machine-readable medium of claim 19, wherein the precoding prediction model is determined from the precoding prediction data based on the detected pattern. 